tabPivEventWide <-
  tabMetabolonUntgtLong %>% 
  # pivot wide to ensure consistent handling 
  # of subject-event-sample and missing
  dcast.data.table(
    formula = treatment + Subject + COMP_ID + BIOCHEMICAL ~ Event, 
    fill = NA, 
    fun.aggregate = mean,
    value.var = "ScaledImpData") %>% 
  # subtract week12 from baseline
  .[, Week12MinusBaseline := Week12 - Baseline] %>% 
  .[, log2Wk12OverBaseline := log2(Week12 / Baseline)]

tabPivEventWideOrigScale <-
  tabMetabolonUntgtLong %>% 
  # pivot wide to ensure consistent handling 
  # of subject-event-sample and missing
  dcast.data.table(
    formula = treatment + Subject + COMP_ID + BIOCHEMICAL ~ Event, 
    fill = NA, 
    # This is needed due to two subjects having a replicate timepoint
    fun.aggregate = mean,
    value.var = "OrigScale") %>% 
  .[, log2RatioOrigScale := log2(Week12 / Baseline)]

tabPivEventWide <-
  tabPivEventWide %>% 
  .[tabPivEventWideOrigScale[, .(Subject, COMP_ID, BIOCHEMICAL, log2RatioOrigScale)], 
    on = .(Subject, COMP_ID, BIOCHEMICAL)]

# Tally sums of non-missing values
tabPivEventWide[, numNonMissing := sum(!is.na(log2RatioOrigScale)), 
                     by = .(treatment, BIOCHEMICAL)]
# Tally sums of non-zero log-ratios
tabPivEventWide[, numNonzeroLogRatio := 
                  sum(log2Wk12OverBaseline != 0.0, na.rm = TRUE),
                by = .(treatment, BIOCHEMICAL)]

tabMetabolonUntgtLong %>% 
  .[(Event == "Baseline")] %>% 
  .[, .(medianSubject = median(ScaledImpData, na.rm = TRUE)), 
    by = .(treatment, BIOCHEMICAL)] %>% 
  # Compute differences with placebo
  .[, .(wbf11 = medianSubject[treatment == "wbf11"] -
          medianSubject[treatment == "placebo"], 
        wbf10 = medianSubject[treatment == "wbf10"] -
          medianSubject[treatment == "placebo"]), 
    by = "BIOCHEMICAL"] %>% 
  melt.data.table(id.vars = "BIOCHEMICAL", 
                  variable.name = "treatment", 
                  variable.factor = FALSE) %>% 
  .[, Treatment := studyArmPretty[treatment]] %>% 
  ggplot(aes(x = value, y = Treatment, fill = Treatment)) +
  geom_blank(mapping = aes(x = -value)) +
  # geom_vline(xintercept = 0.0, size = 0.2, color = "gray") +
  geom_density_ridges(scale = 1.6, rel_min_height = 0.0, alpha = 1.0) +
  # scale_y_discrete(expand = expansion(mult = c(0.0, 0))) +
  scaleFillTreatmentManPretty +
  theme_ridges() +
  # xlim(-2.5, 2.5) +
  xlab("Difference between medians at baseline")

pQqLogRatio <-
  tabMedianDistQs %>% 
  melt.data.table(
    id.vars = c("quantChar", "percentile", "placebo"), 
    variable.name = "studyArm", 
    variable.factor = FALSE, 
    value.factor = FALSE,
    value.name = "log2Ratio") %>% 
  setorder(studyArm, percentile) %>% 
  ggplot(aes(placebo, log2Ratio, color = studyArm)) +
  geom_abline() +
  geom_path(size = 1.5) +
  geom_point(size = 2.5, stroke = 0) +
  scaleColorManualTreatment +
  ylab("Formulation") +
  xlab("Placebo") +
  ggtitle("Q-Q plot") +
  theme(
    legend.position = 'none',
    panel.grid = element_blank())
pQqLogRatio

fviz_eig(pcaBaseline) + 
  theme(
    panel.grid.major.x = element_blank(), 
    axis.ticks = element_blank())

fviz_pca_ind(
  X = pcaBaseline, 
  label = "none", 
  habillage = tabUntgtBaselineWide$treatment %>% factor(),
  addEllipses = TRUE, 
  ellipse.level = 0.95
) +
  scaleColorManualTreatment +
  scaleFillManualTreatment +
  theme(
    legend.position = "none",
    plot.title = element_blank(),
    panel.grid = element_blank()
  ) + 
  ggtitle("PCA, baseline-only") +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )

tabLogRatioWide <-
  tabPivEventWide %>%
  # Pivot even wider
  dcast.data.table(
    formula = treatment + Subject ~ COMP_ID, 
    value.var = "log2Wk12OverBaseline")
tabLogRatioWide[1:5, 1:5, with = FALSE]

# Perform PCA
pcaLogRatio <- PCA(tabLogRatioWide[, -c(1, 2), with = FALSE],  graph = FALSE)

fviz_eig(pcaLogRatio) + 
  theme(
    panel.grid.major.x = element_blank(), 
    axis.ticks = element_blank())

pLogRatioPCA <-
  fviz_pca_ind(
    X = pcaLogRatio, 
    label = "none", 
    habillage = tabLogRatioWide$treatment %>% factor(),
    addEllipses = TRUE, 
    ellipse.level = 0.95
  ) +
  scaleColorManualTreatment +
  scaleFillManualTreatment +
  ggtitle("PCA") +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
pLogRatioPCA

tabWilcoxBwGrpWbf11Placebo <- NULL
# Compute the between-group comparison WBF-011 v. Placebo
tabWilcoxBwGrpWbf11Placebo <-
  tabPivEventWide %>% 
  .[(numNonMissing > 5)] %>% 
  .[(treatment %in% c("wbf11", "placebo"))] %>% 
  .[, .(
    wilcoxOut = list(
      try(expr = {
        wilcox.test(
          x = log2Wk12OverBaseline[(treatment == "wbf11")],
          y = log2Wk12OverBaseline[(treatment == "placebo")],
          alternative = "two.sided",
          conf.int = TRUE,
          paired = FALSE)
      }, silent = TRUE)
    )
  ), 
  by = .(BIOCHEMICAL)]
# Extract key results from test
extract_wi = function(wilcoxOut){
  data.table(
    pvalue = wilcoxOut$p.value,
    statistic = wilcoxOut$statistic,
    estimate = wilcoxOut$estimate,
    confIntLo = wilcoxOut$conf.int[1],
    confIntHi = wilcoxOut$conf.int[2]
  )
}
# Check that test didn't fail and return non-sense (missing or funky data)
tabWilcoxBwGrpWbf11Placebo[, success := inherits(wilcoxOut[[1]], "htest"), by = .(BIOCHEMICAL)]
tabWilcoxBwGrpWbf11Placebo <-
  tabWilcoxBwGrpWbf11Placebo %>% 
  .[(success), try({extract_wi(wilcoxOut[[1]])}, silent = TRUE), 
    by = .(BIOCHEMICAL)]
setorder(tabWilcoxBwGrpWbf11Placebo, -pvalue)

tabWilcoxBwGrpWbf10Placebo <- NULL
# Compute the between-group comparison WBF-011 v. Placebo
tabWilcoxBwGrpWbf10Placebo <-
  tabPivEventWide %>% 
  .[(numNonMissing > 5)] %>% 
  # .[(numNonzeroLogRatio > thresholdMinNonzeroLogRatios)] %>%
  .[(treatment %in% c("wbf10", "placebo"))] %>% 
  .[, .(
    wilcoxOut = list(
      try(expr = {
        wilcox.test(
          x = log2Wk12OverBaseline[(treatment == "wbf10")],
          y = log2Wk12OverBaseline[(treatment == "placebo")],
          alternative = "two.sided",
          conf.int = TRUE,
          paired = FALSE)
      }, silent = TRUE)
    )
  ), 
  by = .(BIOCHEMICAL)]
# Extract key results from test
extract_wi = function(wilcoxOut){
  data.table(
    pvalue = wilcoxOut$p.value,
    statistic = wilcoxOut$statistic,
    estimate = wilcoxOut$estimate,
    confIntLo = wilcoxOut$conf.int[1],
    confIntHi = wilcoxOut$conf.int[2]
  )
}
# Check that test didn't fail and return non-sense (missing or funky data)
tabWilcoxBwGrpWbf10Placebo[, success := inherits(wilcoxOut[[1]], "htest"), by = .(BIOCHEMICAL)]
tabWilcoxBwGrpWbf10Placebo <-
  tabWilcoxBwGrpWbf10Placebo %>% 
  .[(success), try({extract_wi(wilcoxOut[[1]])}, silent = TRUE), 
    by = .(BIOCHEMICAL)]
setorder(tabWilcoxBwGrpWbf10Placebo, -pvalue)

# Curate highlights
tabWilcoxBwGrpWbf11PlaceboAnnot <- NULL
tabWilcoxBwGrpWbf11PlaceboAnnot <- 
  tabMetabolonUntgtLong %>% 
  .[, .SD[1], by = "BIOCHEMICAL"] %>% 
  .[, .(BIOCHEMICAL, SUPER_PATHWAY, SUB_PATHWAY)] %>% 
  .[tabWilcoxBwGrpWbf11Placebo, on = "BIOCHEMICAL", nomatch = 0] %>% 
  copy()
setnames(tabWilcoxBwGrpWbf11PlaceboAnnot, "estimate", "estimateBwGrp")
setnames(tabWilcoxBwGrpWbf11PlaceboAnnot, "pvalue", "pBwGrp")

# First curate key pathways to highlight
tabWilcoxBwGrpWbf11PlaceboAnnot[, subPathway := NA_character_]

## FAO - Fatty Acid Oxidation, Acyl Carnitines
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("Acyl Carnitine", SUB_PATHWAY)]$SUB_PATHWAY %>% 
  unique() %>% sort()

# Define key representative FAO/acylcarnitine subgroups for main plot.
vecCuratedFaoSubpaths <-
  c(
    "Fatty Acid Metabolism (Acyl Carnitine, Medium Chain)",
    "Fatty Acid Metabolism (Acyl Carnitine, Dicarboxylate)",
    "Fatty Acid Metabolism (Acyl Carnitine, Polyunsaturated)",
    "Fatty Acid Metabolism (Acyl Carnitine, Hydroxy)",
    "Fatty Acid Metabolism (Acyl Carnitine, Long Chain Saturated)",
    "Fatty Acid Metabolism (Acyl Carnitine, Monounsaturated)",
    "Medium Chain Fatty Acid",
    # "Fatty Acid, Monohydroxy",
    # "Fatty Acid, Dihydroxy",
    "Fatty Acid, Dicarboxylate",
    "Long Chain Monounsaturated Fatty Acid",
    "Long Chain Polyunsaturated Fatty Acid (n3 and n6)")
tabWilcoxBwGrpWbf11PlaceboAnnot[(SUB_PATHWAY %in% vecCuratedFaoSubpaths),
                                subPathway := "Fatty Acid Oxidation"]

# Must be within lipid super-pathway
tabWilcoxBwGrpWbf11PlaceboAnnot[(
  subPathway == "Fatty Acid Oxidation" & 
    SUPER_PATHWAY != "Lipid"), 
  subPathway := NA]

# This one appears to be elevated in juvenile diabetes, 
# PMID: 2947647 DOI: 10.1002/bms.1200131004
# But possibly derived from threonine and so not relevant to FAO.
tabWilcoxBwGrpWbf11PlaceboAnnot["2R,3R-dihydroxybutyrate", 
                                on = "BIOCHEMICAL", 
                                subPathway := NA_character_]
# Also not part of fatty acid oxidation (just look at the structure)
# Derived from amino acid, or pyridine?
# "Picolinoylglycine belongs to the class of organic compounds known as n-acyl-alpha amino acids..."
# https://hmdb.ca/metabolites/HMDB0059766
tabWilcoxBwGrpWbf11PlaceboAnnot["picolinoylglycine", 
                                on = "BIOCHEMICAL", 
                                subPathway := NA_character_]

# BCAA
# This group was investigated due to prior interest,
# but the data does not support emphasizing this group
tabWilcoxBwGrpWbf11PlaceboAnnot["Leucine, Isoleucine and Valine Metabolism", 
                                subPathway := NA_character_, 
                                on = "SUB_PATHWAY"]
vecCuratedBcaaSubpaths <-
  c("Fatty Acid Metabolism (also BCAA Metabolism)",
    "Fatty Acid, Branched")
tabWilcoxBwGrpWbf11PlaceboAnnot[(SUB_PATHWAY %in% vecCuratedBcaaSubpaths),
                                subPathway := NA_character_]
# propionylcarnitine is indicative of BCAA
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("propionylcarnitine", BIOCHEMICAL), 
                                subPathway := NA_character_]

# Data supports emphasizing tryptophan metabolites
tabWilcoxBwGrpWbf11PlaceboAnnot["Tryptophan Metabolism" , 
                                subPathway := "Tryptophan", 
                                on = "SUB_PATHWAY"]

# non-tryptophan aromatic amino acids.
# This group was investigated due to prior interest,
# but the data does not support emphasizing this group
nonTrpAAA <- "Phe, Tyr"
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("Tyrosine", SUB_PATHWAY), 
                                subPathway := NA_character_]
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("Phenylalanine", SUB_PATHWAY), 
                                subPathway := NA_character_]

# Omit '1-carboxyethyl' metabolites (octopines) from other subgroups,
# where their presence is misleading wrt the pathway
# (they are clearly their own, correlated group)
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("1-carboxyethyl", BIOCHEMICAL), 
                                subPathway := NA_character_]

grepSearchStringBilirubin <- 
  "bilirubin|glucuronate|Urobilin|urobilinogen|stercobilin|heme|biliverdin"
tabWilcoxBwGrpWbf11PlaceboAnnot[grep(grepSearchStringBilirubin, BIOCHEMICAL), 
                                subPathway := "Bilirubin"]
tabWilcoxBwGrpWbf11PlaceboAnnot[grep("Bile", SUB_PATHWAY), 
                                subPathway := "Bile Acids"]

# Define plot
pVolcanoBwGrpWbf11PlaceboBase <- pVolcanoBwGrpWbf11Placebo <- NULL
pVolcanoBwGrpWbf11PlaceboBase <- 
  ggplot(
    mapping = aes(
      x = estimateBwGrp, 
      y = -log10(pBwGrp)
    )) +
  # Horizontally center
  geom_blank(
    mapping = aes(x = -estimateBwGrp), 
    data = tabWilcoxBwGrpWbf11PlaceboAnnot %>% copy() %>% 
      .[, subPathway := NULL] %>% 
      .[, SUB_PATHWAY := NULL]
  ) +
  # Volcano background cloud of all unnamed.
  geom_point(
    # Omit faceting variable so that these show up on every panel
    data = tabWilcoxBwGrpWbf11PlaceboAnnot %>% copy() %>% 
      .[, subPathway := NULL] %>% 
      .[, SUB_PATHWAY := NULL],
    fill = "gray",
    color = "gray",
    stroke = 0,
    size = 1.5, 
    alpha = 0.25) +
  geom_vline(
    xintercept = 0.0, 
    color = "gray",
    size = 0.25
  ) +
  ylab(expression(paste(-log[10](p)))) +
  xlab("WBF-011 - Placebo") +
  labs(color = "Metabolite\nGroup") +
  ggtitle("Between-group comparison, wilcoxon test", 
          "WBF-011 - placebo.") +
  theme(
    strip.text = element_text(size = 10),
    strip.background = element_blank(),
    plot.title = element_blank(),
    plot.subtitle = element_blank(),
    panel.grid = element_blank(),
    axis.title.y = element_text(size = 13, angle = 0, vjust = 0.95),
    axis.title.x = element_text(size = 13, angle = 0, vjust = 1),
    legend.position = "none"
  ) 

vecOrderSubPaths <-
  c("Fatty Acid Oxidation", "Bilirubin", "Bile Acids", "Tryptophan")
thresholdAlphaEmphasisUntgt <- 0.1

# Prepare plot definition
pVolcanoBwGrpWbf11Placebo <-
  pVolcanoBwGrpWbf11PlaceboBase +
    # Highlight!
  # color = subPathway
  geom_point(
    data = (tabWilcoxBwGrpWbf11PlaceboAnnot[!is.na(subPathway)]),
    mapping = aes(
      size = pBwGrp < thresholdAlphaEmphasisUntgt,
      alpha = pBwGrp < thresholdAlphaEmphasisUntgt
    ),
    shape = 21,
    color = "darkgreen",
    fill = "darkgreen",
    stroke = 0.1,
    alpha = 1) +
  scale_size_manual(values = c("TRUE" = 2, "FALSE" = 1)) +
  scale_alpha_manual(values = c("TRUE" = 1, "FALSE" = 0.5)) +
  facet_wrap(~factor(subPathway, levels = vecOrderSubPaths), 
             nrow = 2,
             drop = TRUE)
# Peek at the FAO subgroups
vecFaoShow <-
  tabWilcoxBwGrpWbf11PlaceboAnnot %>% 
  .[(subPathway == "Fatty Acid Oxidation")] %>% 
  .$SUB_PATHWAY %>% table() %>% sort() %>% 
  .[(. > 1)]
pVolcanoBwGrpWbf11PlaceboBase +
    geom_point(
    data = (tabWilcoxBwGrpWbf11PlaceboAnnot %>% 
              .[(subPathway == "Fatty Acid Oxidation")] %>% 
              .[(abs(estimateBwGrp) > 0 | pBwGrp < 1)]),
    shape = 21,
    color = "darkgreen",
    fill = "darkgreen",
    stroke = 0.1,
    size = 2, 
    alpha = 1) +
  facet_wrap(~factor(SUB_PATHWAY), drop = TRUE)

pVolcanoBwGrpWbf11Placebo

tabBwGrpCompare <- NULL
tabBwGrpCompare <- 
  tabWilcoxBwGrpWbf10Placebo %>% copy %>% 
  setnames("estimate", "estWbf10Pbo") %>% 
  setnames("pvalue", "pWbf10Pbo") %>% 
  .[copy(tabWilcoxBwGrpWbf11PlaceboAnnot), on = "BIOCHEMICAL"] %>% 
  .[, A := 0.5 * (abs(estimateBwGrp) + abs(estWbf10Pbo))] %>% 
  .[, M := estimateBwGrp - estWbf10Pbo]

# Show labels on bile acids...
tabBwGrpCompare %>% 
  ggplot(aes(A, M)) +
  geom_point(size = 0.5, color = "gray") +
  geom_point(
    data = tabBwGrpCompare[grep("cholate", BIOCHEMICAL)],
    color = "darkorange") +
  ggrepel::geom_text_repel(
    min.segment.length = 0,
    size = 2,
    mapping = aes(label = BIOCHEMICAL),
    data = tabBwGrpCompare %>% 
      .[grep("cholate", BIOCHEMICAL)] %>% 
      .[(A > 0.4 | abs(M) > 1.0)]) +
  # .[(A > 0.65 | abs(M) > 1.0)]) +
  ggtitle("Between-group comparison with placebo, MA plot")

# Mimic the facet panels from the main figure
tabBwGrpCompare %>% copy %>% 
  .[, subPathway := NULL] %>% 
  ggplot(aes(A, M)) +
  geom_hline(yintercept = 0.0, size = 0.2) +
  geom_point(size = 0.5, color = "gray", stroke = 0) +
  geom_point(
    stroke = 0,
    size = 1,
    data = tabBwGrpCompare[!is.na(subPathway)],
    color = "darkorange") +
  facet_wrap(~subPathway) +
  ylab("WBF-011 - WBF-010") +
  ggtitle("Between-group comparison relative to placebo, MA plot") +
  theme(panel.grid = element_blank())

pVolcanoBwGrpBothVsPlacebo <- NULL
pVolcanoBwGrpBothVsPlacebo <-
  pVolcanoBwGrpWbf11Placebo +
  # Highlight!
  geom_point(
    data = (tabBwGrpCompare[!is.na(subPathway)]),
    mapping = aes(
      x = estWbf10Pbo,
      y = -log10(pWbf10Pbo),
      size = pWbf10Pbo < thresholdAlphaEmphasisUntgt,
      alpha = pWbf10Pbo < thresholdAlphaEmphasisUntgt
    ),
    shape = 21,
    color = "darkblue",
    fill = "darkblue",
    stroke = 0.1,
    alpha = 1) +
  facet_wrap(~factor(subPathway, levels = vecOrderSubPaths), 
             nrow = 2,
             drop = TRUE) +
  xlab("Formulation - Placebo")
pVolcanoBwGrpBothVsPlacebo

tabWilcoxLog2Wk12OverBaselResult <-
  tabPivEventWide %>% 
  # .[(numNonMissing > 0)] %>% 
  .[, .(
    wilcoxOut = list(
      wilcox.test(
        x = log2Wk12OverBaseline, 
        alternative = "two.sided",
        conf.int = TRUE,
        mu = 0.0, 
        paired = FALSE)
    )
  ), 
  by = .(treatment, BIOCHEMICAL)]
# Extract key results from test
extract_wi = function(wilcoxOut){data.table(
  pvalue = wilcoxOut$p.value,
  statistic = wilcoxOut$statistic,
  estimate = wilcoxOut$estimate,
  confIntLo = wilcoxOut$conf.int[1],
  confIntHi = wilcoxOut$conf.int[2]
)}
tabWilcoxLog2Wk12OverBaselResult <-
  tabWilcoxLog2Wk12OverBaselResult[, extract_wi(wilcoxOut[[1]]), 
                                   by = .(treatment, BIOCHEMICAL)]
setnames(tabWilcoxLog2Wk12OverBaselResult, "estimate", "estimateWinGrp")
setnames(tabWilcoxLog2Wk12OverBaselResult, "pvalue", "pWinGrp")

# subtract within-group C.I.s for between-group 'hits'
tabSpecialWinGrp <- NULL
tabSpecialWinGrp <-
  (tabWilcoxBwGrpWbf11PlaceboAnnot %>% copy %>% 
     .[!is.na(subPathway)] %>% 
     # Same as emphasis threshold in Fig 3D
     .[(pBwGrp < thresholdAlphaEmphasisUntgt)] %>%
     .[, .(BIOCHEMICAL, subPathway, estimateBwGrp, pBwGrp)]) %>% 
  tabWilcoxLog2Wk12OverBaselResult[., on = "BIOCHEMICAL"]
# Improve the label for bilirubin degradation products
tabSpecialWinGrp[, 
                 BIOCHEMICAL := gsub(
                   pattern = "bilirubin degradation product, ", 
                   replacement = "bilirubin deriv. ", 
                   BIOCHEMICAL)]
# Define order of metabolites to help with legibility
orderSpecialMetabs <- 
  tabSpecialWinGrp %>% 
  .[(treatment == "wbf11")] %>% 
  setorder(-estimateWinGrp) %>% 
  .$BIOCHEMICAL
tabSpecialWinGrp[, metaboFactor := factor(BIOCHEMICAL, levels = orderSpecialMetabs)]
# Set CI edges to Inf if they are outside of helpful-range
tabSpecialWinGrp[(confIntLo < -1.0), confIntLo := -Inf]
tabSpecialWinGrp[(confIntHi > 1.0), confIntHi := Inf]

pSpecialWinGrp <-
  tabSpecialWinGrp %>% copy %>% 
  # Show the bile acids result in bile-acid figure, 
  # omit here to save figure space
  .[(subPathway != "Bile Acids")] %>% 
  .[, subPathwayFac := factor(
    x = subPathway, 
    levels = vecOrderSubPaths[vecOrderSubPaths != "Bile Acids"])] %>% 
  ggplot(aes(metaboFactor, estimateWinGrp, color = treatment, fill = treatment)) +
  geom_hline(yintercept = 0.0, size = 0.1) +
  facet_grid(cols = vars(subPathwayFac), scales = 'free', space = 'free') +
  # Adding this has the effect of centering each panel
  geom_blank(mapping = aes(ymin = -confIntLo, ymax = -confIntHi)) +
  # The (within-group) log2-ratio + C.I.s
  geom_pointrange(
    alpha = 1.0,
    size = 0.5,
    fatten = 4,
    shape = 23,
    stroke = 0.1,
    position = position_dodge(width = 0.6),
    mapping = aes(ymin = confIntLo, ymax = confIntHi)
  ) +
  ylab(
    expression(paste(
      log[2](over(Endpoint,Baseline))
    ))) +
  scaleColorManualTreatment +
  scaleFillManualTreatment +
  theme(
    legend.position = "none",
    panel.grid = element_blank(),
    strip.background = element_blank(),
    axis.title.y = element_text(size = 7, angle = 0, vjust = 0.5),
    axis.title.x = element_blank(),
    axis.text.x = element_text(
      size = 9,
      angle = 30, 
      hjust = 1, 
      vjust = 1)
  )
pSpecialWinGrp